Note: This article was written in 2015. The predictions it made are now playing out far faster than expected.
Technology-driven jobs are the ultimate frontier of human labour where machines have no place.
This indeed seems hard to contest. Software programmers will still be required to program all of this wonderful artificial intelligence that is going to significantly impact all other jobs right?
Wrong…
Software that writes itself
Software today is as faulty as one doesn't want to imagine, and that is why self-modifying code was invented. Yes, this is software code that modifies itself automatically. Today, the main functionality is usually aimed to improve performance and to reduce the size of the code-base in order to make it easier to maintain.
At the time this was written, it seemed like a far stretch away from computer programs programming additional functionality with minimal or even without intervention of a human being. But that was 2015.
The curve got steeper—much steeper
Code that functionally creates itself needs to be driven by smart machinery, i.e. artificial intelligence. In 2015, that was still part of the future. But as with many exponential changes, the curve of advancement accelerated by the day.
A major milestone crossed in 2014 was a machine passing the Turing Test—a written conversation test where a machine demonstrates intelligence if it can deceive a human into thinking it's human. Back then, it fooled judges by pretending to be a 13-year-old boy.
Fast forward to 2026: that benchmark is ancient history.
We now have Claude, ChatGPT, Gemini and other large language models that not only pass the Turing Test, they're actively reshaping how code is written. GitHub Copilot completes code suggestions. ChatGPT can write, debug and refactor software. These aren't toys—they're production tools millions of developers use every day.
DeepMind (which Google acquired) didn't just build a brain-like computer; it created AlphaFold, which solved a 50-year-old protein-folding problem in biology. It created Gemini, a multimodal AI that rivals and exceeds human performance on many complex reasoning tasks. The frontier has moved far beyond "Hello World."
What about human programmers?
The initial seeds of this evolution that seemed so distant in 2015 are now the norm. Machine learning creates code. AI-assisted development has become standard practice. Entire codebases can be generated, debugged and optimized by algorithms working alongside (or sometimes, entirely independent of) human programmers.
Machines are now automatically creating other machines. Software is automatically creating other software. The barriers we were pushing against have been breached.
For now, software programmers only have to worry about the rapid shifts in programming paradigms and tools that make yesterday's expertise feel obsolete tomorrow. But there's a real possibility—and an accelerating timeline—that these architects of technology will also become replaceable by the very tools they built.
The real question: What happens next?
But here's the thing: this article wasn't written as a warning of doom. It was written as an invitation to a deeper question.
If AI can do the coding, what does that mean for us?
The traditional answer is panic: "Will jobs disappear?" Yes, they will. That's not new. Automation has always displaced work. The question that matters more is this: "What work remains meaningful, and how do we build an economy that supports people through the transition?"
This is where regenerative thinking becomes essential. As AI transforms work, we face a choice. We can resist change and watch the gap between technology and human purpose widen. Or we can ask: What if this freed us to focus on work that machines cannot do—work that requires human presence, wisdom, creativity and care? What if AI handled the algorithmic work while we concentrated on the relational, regenerative work—healing communities, restoring ecosystems, building genuine connection?
A regenerative economy doesn't fight technological change. It directs it toward human and ecological flourishing. When machines handle the coding, humans handle the meaning-making. When algorithms optimize production, humans optimize for well-being. That's not a retreat from progress—it's progress toward something that actually matters.
The challenge is that our current economic system isn't designed for this shift. It assumes work = income, and income = survival. If machines do the work, what happens to people? That's where the Sustainable Money System (SuMSy) becomes relevant: a redesigned economy where people have financial security not because they're employed, but because society recognises their inherent value. Where work can be chosen for meaning, not desperation.
Ready to navigate the future?
The acceleration of AI isn't slowing down. Neither is the need to rethink how we work, earn and find meaning.
Read our blog article "Navigeren in een wereld van versnelling" (Navigating in a World of Acceleration) on happonomy.org/blog for deeper reflection on what it means to stay human in a rapidly changing world.
Or join us for Happonomy Fundamentals Training on happonomy.org/products—designed to help you and your community build resilience and meaning as technology reshapes the landscape of work.
The future isn't something that happens to us. It's something we design.